Research on Match Momentum Prediction Based on the Fusion of LSTM and Neural Network Models

This paper proposes a comprehensive framework that integrates Long Short-Term Memory (LSTM) and neural network models to deeply quantify momentum in tennis matches and explore its important role in predicting match results. Firstly, a momentum indicator system was constructed, including the number of consecutive scores, the score difference with opponents, and the number of consecutive wins, to measure the performance status of players in the game. Subsequently, the Pearson correlation analysis model was used to evaluate the correlation between these momentum indicators and player game results. On this basis, a neural network model was established and optimized through training data to achieve momentum-based prediction of competition results. To further improve prediction accuracy, this article also introduces the LSTM model to handle long-term dependencies in time series data, combined with neural network models to form an efficient and accurate prediction system.